Quality-Aware Data Engineering: Ensuring Trustworthy AI Pipelines

Abstract

This paper presents an integrated framework for quality-aware data engineering aimed at establishing trustworthy AI pipelines. We articulate the critical importance of data quality in trustworthy AI systems and propose a structured method to ensure high-quality data flows throughout AI pipelines—from ingestion to deployment. Our approach draws from recent developments in data pipeline quality, responsible design patterns, and digital twin applications, all grounded in 2023 research. First, we review a taxonomy of factors affecting data pipeline quality, including data types, infrastructure, lifecycle management, and developer workflows arXiv. We also examine the adoption of responsible design patterns for machine learning pipelines targeting ethical and fair outcomes arXiv. Next, we introduce a methodology that integrates this taxonomy and ethical framework into pipeline design, with emphasis on automated validation, monitoring, and governance. We validate our framework through two case studies: a digital twin application that applies quality-aware pipelines in operational contexts SSRN, and a simulated AI pipeline with engineered data quality gates. Results illustrate marked improvements in data reliability, reduced data-related failures, and enhanced robustness and reproducibility of AI outputs. We discuss key challenges—such as handling schema drift, root causes of data issues (e.g., type mismatches and ingestion errors), and aligning engineering and ethical layers. We conclude by highlighting the broader implications for AI trustworthiness and propose future directions, including self-adapting pipelines capable of automatic detection and resolution of data anomalies. Our contributions demonstrate that embedding quality assurance and ethical design into data engineering significantly strengthens the trustworthiness of AI systems.